投稿時間:2023-01-27 18:28:44 RSSフィード2023-01-27 18:00 分まとめ(33件)

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IT	気になる、記になる…	Amazon、Kindleストアで｢サスペンス・ミステリーキャンペーン｣のセールを開始 ｰ 約1,000冊が最大80％オフに	https://taisy0.com/2023/01/27/167673.html	amazon	2023-01-27 08:47:13
IT	気になる、記になる…	Microsoft、｢Surface Duo｣向けに2023年1月のアップデートを配信開始	https://taisy0.com/2023/01/27/167671.html	android	2023-01-27 08:13:49
IT	気になる、記になる…	Microsoft、｢Surface Duo 2｣向けに2023年1月のアップデートを配信開始	https://taisy0.com/2023/01/27/167667.html	microsoft	2023-01-27 08:11:45
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] はとバス、人気キャラクター「リラックマ」とコラボ　限定ラッピングバスも運行	https://www.itmedia.co.jp/business/articles/2301/27/news168.html	itmedia	2023-01-27 17:53:00
IT	ITmedia 総合記事一覧	[ITmedia News] ベートーベンの楽曲に「三三七拍子」は入ってる？　音符36万個を学んだAIで調べる「Beethoven Beats」	https://www.itmedia.co.jp/news/articles/2301/27/news042.html	beats	2023-01-27 17:30:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 一般社員の9割が「インフレ手当がほしい」、希望額は？	https://www.itmedia.co.jp/business/articles/2301/27/news145.html	itmedia	2023-01-27 17:16:00
python	Pythonタグが付けられた新着投稿 - Qiita	監視カメラの映像をOBSでライブ配信していて、Pythonでシーンを自動切り替えできるようにしてみた	https://qiita.com/mix_dvd/items/8f98760c51c69af9dc93	hdmiusb	2023-01-27 17:20:57
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	Ubuntu: Visual Studio Code の使い方	https://qiita.com/ekzemplaro/items/2ff5ba009f9fc6699eb1	codeonli	2023-01-27 17:09:04
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS：EC2インスタンスの情報を取得したいとき	https://qiita.com/If_it_bleeds-we_can_kill_it/items/f445608fdf3286ebcce8	awsec	2023-01-27 17:41:11
技術ブログ	Developers.IO	【書評】「プロジェクトマネジメントの基本が全部わかる本」はPMやる人は必見です！！	https://dev.classmethod.jp/articles/review-basics-of-pm/	素敵	2023-01-27 08:52:27
技術ブログ	Developers.IO	( Google Apps Script ) Objectのキーに変数を使いたいんだが	https://dev.classmethod.jp/articles/456577dbe403214f34529e70f9f6493b6c19a913/	googleappsscriptobject	2023-01-27 08:49:23
海外TECH	DEV Community	Evaluation Metrics for Classification and Regression: A Comprehensive Guide	https://dev.to/anurag629/evaluation-metrics-for-classification-and-regression-a-comprehensive-guide-47hb	Evaluation Metrics for Classification and Regression A Comprehensive GuideMachine learning models are used to make predictions and classify data However it s essential to evaluate the performance of these models to ensure that they are working correctly In this article we will discuss the various evaluation metrics that can be used for classification and regression problems We will also cover the mathematical formulas behind these metrics and provide examples of how to implement them in Python By the end of this article you will have a solid understanding of how to evaluate the performance of your machine learning models Regression Mean Absolute Error MAE Mean Squared Error MSE R squared R² Root Mean Squared Error RMSE Mean Absolute Percentage Error MAPE Classification AccuracyPrecisionRecallF ScoreAUC ROC CurveConfusion Matrix Evaluation metrics for regression Mean Absolute Error MAE This metric calculates the average absolute difference between the predicted values and the actual values The lower the MAE the better the model s predictions In Python you can use the mean absolute error function from the sklearn metrics library to calculate the MAE This formula calculates the average absolute difference between the true values and the predicted values for all samples The smaller the MAE the better the model s performance from sklearn metrics import mean absolute errory true y pred print mean absolute error y true y pred Mean Squared Error MSE This metric calculates the average of the squared differences between the predicted values and the actual values Like the MAE the lower the MSE the better the model s predictions In Python you can use the mean squared error function from the sklearn metrics library to calculate the MSE The mean squared error is a measure of the difference between the true and predicted values where the difference is squared to penalize larger errors more heavily The MSE is commonly used as a loss function in machine learning and is often used to evaluate the performance of regression models from sklearn metrics import mean squared errory true y pred print mean squared error y true y pred R squared R² This metric also known as the coefficient of determination ranges from to and represents the proportion of the variance in the dependent variable that is predictable from the independent variable s An R²of indicates that the model perfectly predicts the target variable while an R²of indicates that the model does not predict the target variable at all In Python you can use the r score function from the sklearn metrics library to calculate the R² The numerator of the formula represents the residual sum of squares RSS which is the sum of the squares of the differences between the actual and predicted values of the dependent variable The denominator of the formula represents the total sum of squares TSS which is the sum of the squares of the differences between each value of the dependent variable and the mean value of the dependent variable R squared ranges between and A value of indicates that the model does not explain any of the variance in the dependent variable while a value of indicates that the model explains all of the variance in the dependent variable from sklearn metrics import r scorey true y pred print r score y true y pred Root Mean Squared Error RMSE This metric calculates the square root of the MSE It is useful because it is in the same units as the target variable so it is easier to interpret than the MSE In Python you can use the mean squared error function from the sklearn metrics library to calculate the MSE and then take the square root of that value to get the RMSE In this formula the RMSE is the square root of the average of the squared differences between the true values and the predicted values It is used to measure the difference between the predicted values and the true values and it is widely used in regression models It gives an idea of the average difference between the predicted values and the true values The smaller the RMSE the better the model is from sklearn metrics import mean squared errorimport numpy as npy true y pred print np sqrt mean squared error y true y pred Mean Absolute Percentage Error MAPE It represents the difference between actual value and predicted value in percentage It is useful when you want to know the percentage of error from actual value In this formula the absolute value is taken on the percentage error between the actual and predicted values to ensure that the error is always positive The percentage error is then averaged across all samples to get the mean percentage error def mean absolute percentage error y y pred y y pred np array y np array y pred return np mean np abs y y pred y y true y pred print mean absolute percentage error y true y pred Evaluation metrics for classification Accuracy This metric represents the proportion of correctly classified instances out of the total number of instances It is defined as number of correct predictions number of total predictions In Python you can use the accuracy score function from the sklearn metrics library to calculate the accuracy from sklearn metrics import accuracy scorey true y pred print accuracy score y true y pred Precision This metric represents the proportion of true positive predictions out of all positive predictions It is defined as number of true positives number of true positives number of false positives In Python you can use the precision score function from the sklearn metrics library to calculate the precision from sklearn metrics import precision scorey true y pred print precision score y true y pred Recall This metric represents the proportion of true positive predictions out of all actual positive instances It is defined as number of true positives number of true positives number of false negatives In Python you can use the recall score function from the sklearn metrics library to calculate the recall from sklearn metrics import recall scorey true y pred print recall score y true y pred F Score It is the harmonic mean of precision and recall F score tries to find the balance between precision and recall It gives a good idea of how precise and robust the classifier is from sklearn metrics import f scorey true y pred print f score y true y pred AUC ROC Curve Receiver Operating Characteristic ROC curve is a graphical representation of the performance of a classification model It plots the true positive rate TPR against the false positive rate FPR at various threshold settings The area under the ROC curve AUC is a measure of how well a parameter can distinguish between two diagnostic groups AUC ranges in value from to A model whose predictions are wrong has an AUC of one whose predictions are correct has an AUC of The formula for the area under the receiver operating characteristic ROC curve AUC is defined as the integral of the true positive rate TPR with respect to the false positive rate FPR over the range of possible threshold values Mathematically it can be represented as from sklearn metrics import roc auc scorey true y pred print roc auc score y true y pred Confusion Matrix It is a table that is used to define the performance of a classification algorithm It is mostly used for binary classification The top left value represents true negatives top right value represents false positives bottom left value represents false negatives and bottom right value represents true positives It is a very useful tool for understanding the performance of a classification model from sklearn metrics import confusion matrixy true y pred print confusion matrix y true y pred Summary In this article we discussed the various evaluation metrics that can be used for classification and regression problems including accuracy precision recall F score R squared and the confusion matrix We also covered the mathematical formulas behind these metrics and provided examples of how to implement them in Python By understanding these evaluation metrics you can ensure that your machine learning models are working correctly and make informed decisions about how to improve their performance Remember that the choice of metrics can be different depending on the problem and the data so it s always a good idea to experiment with different metrics to find the one that best suits your needs GitHub link Complete Data Science Bootcamp Main Post Complete Data Science Bootcamp	2023-01-27 08:20:00
医療系	医療介護 CBnews	ファイザー社ワクチンの有効期間の延長周知を-厚労省が都道府県などに事務連絡	https://www.cbnews.jp/news/entry/20230127174332	予防接種	2023-01-27 17:50:00
医療系	医療介護 CBnews	看護職員のコロナ関連欠勤者数が2週連続減少-厚労省が重点医療機関の集計更新	https://www.cbnews.jp/news/entry/20230127171837	医療機関	2023-01-27 17:25:00
金融	JPX マーケットニュース	[OSE] RNプライム指数構成銘柄の一部変更	https://www.jpx.co.jp/news/2020/20230127-02.html	osern	2023-01-27 18:00:00
金融	JPX マーケットニュース	[東証]特設注意市場銘柄の指定及び上場契約違約金の徴求：ルーデン・ホールディングス（株）	https://www.jpx.co.jp/news/1023/20230127-13.html	特設注意市場銘柄	2023-01-27 17:20:00
金融	ニッセイ基礎研究所	フィリピン経済：22年10-12月期の成長率は前年同期比7.2％増～リベンジ消費により高成長を維持	https://www.nli-research.co.jp/topics_detail1/id=73734?site=nli	フィリピン経済年月期の成長率は前年同期比増リベンジ消費により高成長を維持年月期の実質GDP成長率は前年同期比増前期同増と低下したものの、市場予想同増を上回る結果となった図表。	2023-01-27 17:45:29
海外ニュース	Japan Times latest articles	Alleged ringleader of serial robberies in Japan operated from Manila detention center	https://www.japantimes.co.jp/news/2023/01/27/national/crime-legal/luffy-robberies-manila-detention-center/	Alleged ringleader of serial robberies in Japan operated from Manila detention centerThe suspected mastermind who is said to have gone by the name “Luffy sent instructions via smartphone taking advantage of lax security at the detention	2023-01-27 17:14:02
ニュース	BBC News - Home	Djokovic's father to watch match on TV after Putin row	https://www.bbc.co.uk/sport/tennis/64422557?at_medium=RSS&at_campaign=KARANGA	Djokovic x s father to watch match on TV after Putin rowSrdjan Djokovic will watch his son Novak s Australian Open semi final from home to avoid causing disruption after being pictured with supporters of Russian president Vladimir Putin	2023-01-27 08:12:07
ニュース	BBC News - Home	St John's Wood: Historic church destroyed in large blaze	https://www.bbc.co.uk/news/uk-england-london-64421569?at_medium=RSS&at_campaign=KARANGA	everywhere	2023-01-27 08:20:59
ビジネス	不景気.com	神奈川の運送業「Ce-next」に破産開始決定、負債5億円 - 不景気com	https://www.fukeiki.com/2023/01/ce-next.html	cenext	2023-01-27 08:32:51
ニュース	Newsweek	ベラ・ハディッド、「貝殻ビキニ」でビーチの視線をかっさらう	https://www.newsweekjapan.jp/stories/world/2023/01/post-100700.php	日替わり	2023-01-27 17:57:19
ニュース	Newsweek	世界初「ロボット弁護士」が来月、法廷で人間を弁護する	https://www.newsweekjapan.jp/stories/world/2023/01/ai-69.php	産声	2023-01-27 17:55:52
ビジネス	プレジデントオンライン	乗客の｢10時間車内閉じ込め｣は十分に避けられた…JR西日本が犯した｢3つの判断ミス｣ - 楽観主義､縦割り主義､小手先の対策で大惨事に	https://president.jp/articles/-/65924	判断ミス	2023-01-27 18:00:00
ビジネス	プレジデントオンライン	朝ドラをやめて受信料を下げたほうがいい…｢太りすぎたNHK｣には今すぐ分割･民営化が必要だ - NHKが娯楽番組を放送する意味はあるのか	https://president.jp/articles/-/65896	娯楽番組	2023-01-27 18:00:00
マーケティング	MarkeZine	dely、インフルエンサーを活用した「TRILL mateレビュー企画メニュー」を提供開始	http://markezine.jp/article/detail/41138	提供開始	2023-01-27 17:15:00
IT	週刊アスキー	『戦場のフーガ2』が5月11日に発売決定！公式PVも公開中	https://weekly.ascii.jp/elem/000/004/122/4122432/	nintendo	2023-01-27 17:45:00
IT	週刊アスキー	NTTドコモと慶大、5G高信頼低遅延通信を経由し、ヒトの手を動かして感じ取れる手応えをロボット上で再現する実証実験に成功	https://weekly.ascii.jp/elem/000/004/122/4122417/	実証実験	2023-01-27 17:30:00
IT	週刊アスキー	大塚商会、「DX統合パッケージ」に「freee会計」を連携させた「DX統合パッケージ with freee」発表	https://weekly.ascii.jp/elem/000/004/122/4122419/	freee	2023-01-27 17:30:00
IT	週刊アスキー	丸紅、法人向けフルMVNOサービス「M-Air」提供開始	https://weekly.ascii.jp/elem/000/004/122/4122424/	提供開始	2023-01-27 17:20:00
IT	週刊アスキー	CAT6A対応の自作用LANケーブル100m／300m、サンワサプライ	https://weekly.ascii.jp/elem/000/004/122/4122416/	長さ	2023-01-27 17:10:00
IT	週刊アスキー	シャープ、各種データをクラウド上で管理・共有できるスマートオフィスサービス「COCORO OFFICE 共有フォルダー」発表	https://weekly.ascii.jp/elem/000/004/122/4122422/	cocorooffice	2023-01-27 17:10:00
マーケティング	AdverTimes	荏原製作所のメタバースインターンシップ、多様性推進が背景に	https://www.advertimes.com/20230127/article410101/	荏原製作所	2023-01-27 08:18:54